Quantitative measurement of component accessibility and product assemblability for design for assembly application

Lessons learned from the STS-120/ISS 10A robotics operations

Several Design for Assembly (DFA) methodologies have already been introduced both in research and industry, among which methodologies having quantitative assemblability evaluations are more favored in industry. The evaluation criteria involved in such quantitative measures are only concerned with the difficulty of performing assembly operations one by one. These measures are then combined by a formula to find the total product evaluation. Accordingly, the designer can improve the total assemblability by redesigning the parts that caused bad measures. It can be observed that a major effect of cascading several assembly operations in a certain order on the remaining operations to complete the product assembly is neglected by most DFA evaluation methodologies. Actually, this effect might result in serious difficulties while generating the assembly sequence usually done at a later stage in most industrial firms. To overcome these difficulties, this paper introduces some elementary insights to the integration of assembly design and planning. A systematic feature-based method is introduced to find the precedence assembly constraints and the global evaluation constraints resulting from operations cascading orders. A modified genetic algorithm that finds the best assembly sequence based on these constraints and on the traditional DFA evaluations is then introduced to be included in the DFA procedure. This genetic planner generates a more representative evaluation of the product assemblability by considering the effects of operations orders. As a result, feedback reflecting the actual assembly process difficulties can be given to the designer to improve the product design, while simultaneously generating the best sequence to assemble the product.

In a multi-plant collaborative manufacturing system, the manufacturing operations and assembly operations for producing a product can be distributed at different plants at various locations. The components are assembled with assembly operations performed in a multi-plant assembly sequence. In this research, a multi-plant assembly sequence planning model is presented by integrating (1) assembly sequence planning, and (2) plant assignment. In assembly sequence planning, the components and assembly operations are sequenced according to the assembly constraints and assembly cost objectives. In plant assignment, the components and assembly operations are assigned to the suitable plants to achieve multi-plant cost objectives. The feasible assembly sequences are generated using the developed graph-based models of assembly precedence graphs and matrices. A genetic algorithm (GA) method is presented to evaluate the multi-plant assembly sequences with an objective of minimizing the total of assembly operational costs and multi-plant costs. The main contribution lies in the new model for multi-plant assembly sequence planning and the new GA encoding scheme for simultaneous assembly sequence planning and plant assignment. Example products are tested and discussed. The test results show that the presented method is feasible and efficient for solving the multi-plant assembly sequence planning problem.

Supply chains have been more serious for environmental issues for not only the reduction of CO2 emission but also for material starvation. To enhance a closed loop supply chain for assembled product, a disassembly system is required. At end-of-life, the assembly products at recycling organization should be disassembled environmentally and economically for not only low carbonization but also of material circulation. This recycling and reusing of product enables to reduce CO2 release because the virgin materials which release CO2 volumes caused by production and logistics can be saved by the usage of the recovered materials. Parts with higher recycling and CO2 saving rates ought to be chosen and physically disassembled, but the manual disassembly brings a higher cost, rather than discarding the parts. Therefore, material recovery value is also to be considered for parts selection during the assembly operation. Parts information like material recovery value, CO2 release, weight, precedence relationships are required for environmental and economic parts selection. This study proposes a CAD based approach for environmental and economic parts selection for disassembly using solid works sustainability. It considers higher CO2 saving rate, higher recycling rate and higher material recovery value as environmental and economic criteria for parts selection.

Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot's proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution.

Implications of scale effect for the prediction of high angle of attack aerodynamics

Turbofan aero-engine efficiency evaluation: An integrated approach using VSBM two-stage network DEA

There are various configurations and parameters that contribute to the Design of Unmanned Aerial Vehicles for specific applications. This paper deals with an innovative design of an unmanned aerial vehicle for a specified class of UAVs that require demands such as long endurance, minimized landing space with vertical take-off and landing (VTOL) capabilities. The focal point of this design is superimposing the high endurance blended wing design into tri-copter to address these parameters. The preliminary calculations are initially performed for the blended wing VTOL vehicle based on the required payload capacity and endurance. Superimposing the tri-copter will decrease the aerodynamic efficiency of the vehicle. Computational fluid dynamics (CFD) is a branch of fluid mechanics that uses numerical methods and algorithms to solve complex problems involving fluid flow which will effectively employed to reduce the cost and time during the conceptual and preliminary design stages. CFD analysis was carried out to estimate the major parameters like lift, drag, lift coefficient (CL) and drag coefficient (CD) for various Angle of Attack (AoA) for configurations of blended wing vehicle with and without tri-copter system in the cruise condition. Thus, the vehicle design and propulsion system is effectively optimized using this drag estimation.

Study of the electric field distribution of various electrospinning geometries and its effect on the resultant nanofibers using finite element simulation

In an increasingly competitive and globalized world, companies need effective training methodologies and tools for their employees. However, selecting the most suitable ones is not an easy task. It depends on the requirements of the target group (namely time restrictions), on the specificities of the contents, etc. This is typically the case for training in Lean, the waste elimination manufacturing philosophy. This paper presents and compares two different approaches to lean training methodologies and tools: a simulation game based on a single realistic manufacturing platform, involving production and assembly operations that allows learning by playing; and a digital game that helps understand lean tools. This paper shows that both tools have advantages in terms of trainee motivation and knowledge acquisition. Furthermore, they can be used in a complementary way, reinforcing the acquired knowledge.

This paper discusses a digital twin-based approach for designing and redesigning flexible assembly systems. The digital twin allows modeling the parameters of the production system at different levels including assembly process, production station, and line level. The approach allows dynamically updating the digital twin in runtime, synthesizing data from multiple 2D–3D sensors in order to have up-to-date information about the actual production process. The model integrates both geometrical information and semantics. The model is used in combination with an artificial intelligence logic in order to derive alternative configurations of the production system. The overall approach is discussed with the help of a case study coming from the automotive industry. The case study introduces a production system integrating humans and autonomous mobile dual arm workers.

The design process of a reliable system is, by nature, iterative. Traditional approaches to the design of a reliable system follow the requirement determination, preliminary design, analysis, evaluation, and redesign stages until what is regarded as an acceptable design is achieved. The system requirements typically consist of requirements on reliability, cost, weight, power consumption, physical size, etc. Within available resources, there can exist numerous approaches that completely satisfy all the design requirements. However as modern systems are becoming more and more complex, it is difficult to enumerate all the acceptable designs to find the optimal design configuration. A design optimization tool is greatly needed. This paper embeds a genetic algorithm (GA) into a fault tree method to determine the heuristic optimal design configuration of a reliable system. For optimization, a fault tree which can represent the failure causes of potential designs is used. Two new gates (CHO & RED) are introduced in this research. GA are developed and integrated into a fault-tree solver to find the optimal design. Improvement techniques to accelerate GA convergence and to avoid the GA-premature problem are implemented. Multi-objective optimization is discussed and methods for it are developed. Several techniques to accelerate the optimization process are implemented which appreciably reduce the calculation time. Simulation results show that the integration of GA optimization capabilities with fault-tree reliability-analysis provides a robust, powerful system-design tool. The methodology is applied to an example of a cardiac-assist system.

In the context of the demand for mass customization of products, a trade-off between highly efficient automated systems and flexible manual operators is sought. The linear arrangement of workstations made it possible to divide the process into many simple operations, which increases production efficiency, but also results in an increase in the number of workstations and a significant extension of the line. A human operator is usually treated as a quasi-mechanical object, and a human error is considered, similarly, as a failure of a technical component. However, human behavior is more complex and difficult to predict. A mathematical model of a new production organization is presented, including dividing the traditional production line into shorter sections or replacing the serial assembly line with a U-line with cells. Moreover, the reliability of operator and technical means are distinguished. Work-in-progress inventories are located between line sections to improve system stability. The stability of the assembly line is examined based on the system configuration and probabilistic estimates of human failure. The influence of the symmetry of reliability parameters of people on key performance indicators (KPI (headcount), KPI (surface) and KPI (Overall Equipment Effectiveness) is examined. KPI (solution robustness) and KPI (quality robustness) are also presented in order to evaluate the impact of a disruption on the assembly line performance. New rules for assigning tasks to stations are proposed, taking into account the risk of disruptions in the execution of tasks. For comparison of assembly problems, heuristic methods with newly developed criteria are used. The results show the impact of symmetry/asymmetry on assembly line performance and an asymmetric distribution of manual assembly times that is significantly skewed to the right due to human errors. On the assembly line, the effects of these errors are cumulative and lead to longer assembly times and lower KPIs.

Different methods currently employed for choosing and analyzing model configurations of binary systems with isomorphous substitution in the sublattice were considered. A new more efficient algorithm was proposed for determining the most disordered atomic configuration of an arbitrary composition for a cell with any size. The algorithm was implemented in the form of a computer program which makes it possible to approach the most optimum topology of the arrangement of solid solution components over atomic positions. The program was tested using a number of binary systems and can be recommended for ab initio calculations and simulation with semiempirical methods. Moreover, this approach can be used to decompose complex experimental spectra of isomorphous mixtures which are difficult to interpret without recourse to model concepts regarding the local structure of multicomponent systems.

The UMass Software Development Laboratory (SDL), in collaboration with our colleagues in the Arcadia consortium [7], is working toward the development of advanced software environments. An important goal of this work is to support research on software processes. It is increasingly clear that such research must be based on experimental, exploratory, prototyping activities. It is equally clear that process prototyping will require a corresponding ability to rapidly produce and easily modify prototype software environments. As a result, one important element of SDL's environment research has been the development of appropriate tools and techniques to facilitate environment prototyping.